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Updated: Aug 6, 2026

Probing and Mapping Electrode Surfaces in Solid Oxide Fuel Cells
Published on: September 20, 2012
Recent advances in understanding surface reconstruction for oxygen evolution reaction electrocatalysts: insights from
Sung Uk Chai1, Yoonjun Cho1, Ki Chul Kim2
1Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, The Republic of Korea. lutts@yonsei.ac.kr.
None:
Surface reconstruction has emerged as a defining feature of oxygen evolution reaction (OER) electrocatalysts because many materials operate not as their as-synthesized structures but as dynamically transformed interfacial phases generated under anodic conditions. Density functional theory (DFT) has played a central role in clarifying this behavior by linking adsorption thermodynamics, electronic-structure evolution, defect energetics, lattice-oxygen activity, and dissolution or redeposition processes to the formation of reconstructed active surfaces. In this review, we summarize recent progress in DFT modeling of OER-related surface reconstruction, beginning with established descriptions of OER energetics, adsorbate evolution, lattice-oxygen mechanisms, and scaling relations. We then discuss how reconstruction is initiated by coupled factors, including high-valence-state formation, metal-oxygen covalency, oxygen vacancies, ion mobility, electrolyte composition, and interfacial microenvironments. Representative reconstruction pathways are examined across non-oxide precatalysts, oxides, and oxyhydroxides, with emphasis on how DFT distinguishes the pristine precursor from the true operative surface. We further highlight reconstruction-induced mechanistic shifts between adsorbate-centered and lattice-oxygen-involving pathways, as well as the additional constraints imposed by acidic media, seawater electrolysis, and industrial current densities. Finally, we outline future directions for predictive modeling, including constant-potential approaches, explicit solvation, ab initio molecular dynamics (AIMD), machine-learning interatomic potentials, microkinetics, and structure-search strategies. This review emphasizes that DFT should move beyond static catalyst screening toward understanding and directing controlled surface evolution under realistic OER conditions.
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